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rollbackEdits.php mass rollback
$R(a) = const(a) + \displaystyle\sum_{ s \in Y \setminus \{t\} }\int_{A_s}(g_s(x)−g_t(x))dx$.
Минимум интегрла интеграла достигается, когда $A_s$ совпадает с областью неположительности подынтегрального выражения.
:<tex> A_s=\{x \in X \mid g_s(x) \leq g_t(x), \forall t \in Y, t \leq s\}. </tex>
eval.crossValidateModel(nb, dataset, 10, new Random(41));
System.out.println("Estimated Accuracy: "+ Double.toString(eval.pctCorrect()));
=== Пример на языке R ===
{{Main|Примеры кода на R}}
<font color="gray"># importing package and it's dependencies</font>
library(e1071)
<font color="gray"># reading data</font>
data <- read.csv(<font color="green">"input.csv"</font>, <font color="#660099">sep</font> = <font color="green">','</font>, <font color="#660099">header</font> = FALSE)
<font color="gray"># splitting data into training and test data sets</font>
index <- createDataPartition(<font color="#660099">y</font> = data$<strong><font color="#660E7A">target</font></strong>, <font color="#660099">p</font> = <font color="blue">0.8</font>, <font color="#660099">list</font> = FALSE)
training <- data[index,]
testing <- data[-index,]
<font color="gray"># create objects x and y for predictor and response variables</font>
x <- training[, -<font color="blue">9</font>]
y <- training$<strong><font color="#660E7A">target</font></strong>
<font color="gray"># training model</font>
model <- train(x, y, <font color="green">'nb'</font>, <font color="#660099">trControl</font> = trainControl(<font color="#660099">method</font> = <font color="green">'cv'</font>, <font color="#660099">number</font> = <font color="blue">10</font>))
<font color="gray"># predicting results</font>
predictions <- predict(model, <font color="#660099">newdata</font> = testing)
==См. также==
